ATD: Algorithms for Point Processes on Networks for Threat Detection

ATD：用于威胁检测的网络点处理算法

• 批准号: 1925263
• 负责人: Xiaojing Ye
• 金额: $ 19.97万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925263&HistoricalAwards=false
• 关键词: ATD; Algorithms; Point; Processes; Networks

We live in a world full of networks: contact and social networks connect us to our family, friends and colleagues; computer networks such as Internet allow us to access huge amount of data and information remotely; traffic and logistical networks deliver people, water/food, and all kinds of goods faster than ever before. While we enjoy the conveniences brought by these networks, we must also be aware of the threats and harms if they get jeopardized by, for example, infectious virus, cyber-attacks, etc. The goal of this project is to develop computational algorithms for automated early threat detection based on novel and rigorous mathematical modeling and data analysis concepts. In particular, the activities generated by human and other sources on these networks are modeled as the so-called interactive stochastic point processes. These dynamics are studied and inferred in a mathematical framework of jump stochastic differential equations, which is further extended to integrate mean-field approximation and deep learning techniques that fully leverage the existing big data for fast and accurate threat detection. This project will exploit three closely related computational problems in-depth: influence prediction, optimal sensor allocation, and source identification, all of which are fundamental in threat detection applications on large, heterogeneous, real-world networks.This project will exploit two novel approaches to influence prediction based on a jump stochastic differential equation (JSDE) formulation and an integration of mean field approximation and deep learning techniques. The JSDE formulation yields a concise and exact mathematical formulation of the temporal point process that takes into account the known network structure and mechanism of epidemic spread; and the deep neural mean field approach deduced from JSDE formulation maps the classical difference method in numerical analysis into a structured multi-layer residual network, where the unknown bias of mean field approximation can be effectively learned from observed cascade data for rapid influence prediction. These prediction algorithms will be used in the optimal sensor allocation and epidemic source identification problems for threat detection and mitigation. The results produced in this project are expected to make significant contributions to our understanding of interdependent activities on large-scale heterogeneous networks and the development of new, efficient algorithms for threat detection. The outcomes of the project include novel computational techniques, rigorous mathematical theory and analysis, and efficient numerical algorithms for threat detection applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

我们生活在一个充满网络的世界：联系和社交网络将我们与家人、朋友和同事联系起来；互联网等计算机网络使我们能够远程访问大量数据和信息；交通和物流网络比以往任何时候都更快地运送人员、水/食物和各种商品。在享受这些网络带来的便利的同时，我们也必须意识到如果它们受到例如传染性病毒、网络攻击等的威胁和危害。该项目的目标是基于新的和严格的数学建模和数据分析概念开发用于自动早期威胁检测的计算算法。特别地，人类和其他来源在这些网络上产生的活动被建模为所谓的交互随机点过程。在跳跃随机微分方程的数学框架中对这些动态进行了研究和推断，该框架进一步扩展到集成平均场近似和深度学习技术，充分利用现有的大数据来快速准确地检测威胁。这个项目将深入研究三个密切相关的计算问题：影响预测、传感器最优分配和源识别，所有这些都是大型、异质、真实世界网络上的威胁检测应用程序的基础。本项目将使用两种新的方法来进行影响预测，该方法基于跳跃随机微分方程(JSDE)公式以及平均场近似和深度学习技术的集成。JSDE公式给出了考虑已知网络结构和疫情传播机制的简明而精确的时间点过程的数学公式；而由JSDE公式推导出的深度神经平均场方法将数值分析中的经典差分法映射到结构化的多层残差网络中，其中平均场近似的未知偏差可以从观测的级联数据中有效地学习以用于快速影响预测。这些预测算法将用于传感器的最优分配和疫源识别问题，以进行威胁检测和缓解。该项目的成果有望对我们理解大规模异质网络上相互依赖的活动以及开发新的、有效的威胁检测算法做出重大贡献。该项目的成果包括新颖的计算技术、严谨的数学理论和分析以及用于威胁检测应用的高效数值算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Numerical solution of inverse problems by weak adversarial networks

• DOI: 10.1088/1361-6420/abb447
• 发表时间: 2020-02
• 期刊: Inverse Problems
• 影响因子: 2.1
• 作者: Gang Bao;X. Ye;Yaohua Zang;Haomin Zhou

High-Dimensional Optimal Density Control with Wasserstein Metric Matching

• DOI: 10.1109/cdc49753.2023.10384042
• 发表时间: 2023-07
• 期刊: 2023 62nd IEEE Conference on Decision and Control (CDC)
• 作者: Shaojun Ma;Mengxue Hou;X. Ye;Haomin Zhou

A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

• DOI: 10.48550/arxiv.2204.03804
• 发表时间: 2022-04
• 作者: Wanyu Bian;Qingchao Zhang;X. Ye;Yunmei Chen

Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstruction

• DOI: 10.48550/arxiv.2306.02644
• 发表时间: 2023-06
• 作者: Chi-Jiao Ding;Qingchao Zhang;Ge Wang;X. Ye;Yunmei Chen

Acceleration techniques for level bundle methods in weakly smooth convex constrained optimization

弱光滑凸约束优化中水平束方法的加速技术

• DOI: 10.1007/s10589-020-00208-9
• 发表时间: 2020
• 期刊: Computational optimization and applications
• 影响因子: 2.2
• 作者: Chen, Yunmei;Ye, Xiaojing;Zhang, Wei
• 通讯作者: Zhang, Wei